Shaily Jain

Work place: Department of Computer Science & Engineering, Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India

E-mail: shaily.jain@chitkara.edu.in

Website:

Research Interests:

Biography

Dr. Shaily is PhD in Computer Science in the year 2014. Prior to this she had her Masters in technology in 2009. She has a total of 18 years of experience in teaching. Her research interests include multiprocessor systems on chip, embedded systems, wireless networks, security in networks, data mining and Education Engineering. She is student branch coordinator and member in management of Computer society of India (CSI). She is senior member of IEEE and member of ACM and ISTE. She has in total 42 SCI, reputed international journals and conference papers in her credits. She has filed 10 patents also.  She has guided 10 M.Tech research students and 6 PhD students. She is also working as Assistant Faculty in IUCEE IIEECP program in India. She has been recognized for her contribution in Teaching Learning by IUCEE and an IGIP certified faculty.  Her current affiliation is Professor and Dean (CSE-AE) in Chitkara University, Punjab.

Author Articles
Enhancing Suicide Risk Prediction through BERT: Leveraging Textual Biomarkers for Early Detection

By Karan Bajaj Mukesh Kumar Shaily Jain Vivek Bhardwaj Sahil Walia

DOI: https://doi.org/10.5815/ijisa.2025.02.06, Pub. Date: 8 Apr. 2025

Suicide remains a critical global public health issue, claiming vast number of lives each year. Traditional assessment methods, often reliant on subjective evaluations, have limited effectiveness. This study examines the potential of Bidirectional Encoder Representations from Transformers (BERT) in revolutionizing suicide risk prediction by extracting textual biomarkers from relevant data. The research focuses on the efficacy of BERT in classifying suicide-related text data and introduces a novel BERT-based approach that achieves state-of-the-art accuracy, surpassing 97%. These findings highlight BERT's exceptional capability in handling complex text classification tasks, suggesting broad applicability in mental healthcare. The application of Artificial Intelligence (AI) in mental health poses unique challenges, including the absence of established biological markers for suicide risk and the dependence on subjective data, which necessitates careful consideration of potential biases in training datasets. Additionally, ethical considerations surrounding data privacy and responsible AI development are paramount. This study emphasizes the substantial potential of BERT and similar Natural Language Processing (NLP) techniques to significantly improve the accuracy and effectiveness of suicide risk prediction, paving the way for enhanced early detection and intervention strategies. The research acknowledges the inherent limitations of AI-based approaches and stresses the importance of ongoing efforts to address these issues, ensuring ethical and responsible AI application in mental health.

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